Neighbourhood-level socio-demographic characteristics and risk of COVID-19 incidence and mortality in Ontario, Canada: A population-based study

Objectives We aimed to estimate associations between COVID-19 incidence and mortality with neighbourhood-level immigration, race, housing, and socio-economic characteristics. Methods We conducted a population-based study of 28,808 COVID-19 cases in the provincial reportable infectious disease surveillance systems (Public Health Case and Contact Management System) which includes all known COVID-19 infections and deaths from Ontario, Canada reported between January 23, 2020 and July 28, 2020. Residents of congregate settings, Indigenous communities living on reserves or small neighbourhoods with populations <1,000 were excluded. Comparing neighbourhoods in the 90th to the 10th percentiles of socio-demographic characteristics, we estimated the associations between 18 neighbourhood-level measures of immigration, race, housing and socio-economic characteristics and COVID-19 incidence and mortality using Poisson generalized linear mixed models. Results Neighbourhoods with the highest proportion of immigrants (relative risk (RR): 4.0, 95%CI:3.5–4.5) and visible minority residents (RR: 3.3, 95%CI:2.9–3.7) showed the strongest association with COVID-19 incidence in adjusted models. Among individual race groups, COVID-19 incidence was highest among neighbourhoods with the high proportions of Black (RR: 2.4, 95%CI:2.2–2.6), South Asian (RR: 1.9, 95%CI:1.8–2.1), Latin American (RR: 1.8, 95%CI:1.6–2.0) and Middle Eastern (RR: 1.2, 95%CI:1.1–1.3) residents. Neighbourhoods with the highest average household size (RR: 1.9, 95%CI:1.7–2.1), proportion of multigenerational families (RR: 1.8, 95%CI:1.7–2.0) and unsuitably crowded housing (RR: 2.1, 95%CI:2.0–2.3) were associated with COVID-19 incidence. Neighbourhoods with the highest proportion of residents with less than high school education (RR: 1.6, 95%CI:1.4–1.8), low income (RR: 1.4, 95%CI:1.2–1.5) and unaffordable housing (RR: 1.6, 95%CI:1.4–1.8) were associated with COVID-19 incidence. Similar inequities were observed across neighbourhood-level sociodemographic characteristics and COVID-19 mortality. Conclusions Neighbourhood-level inequities in COVID-19 incidence and mortality were observed in Ontario, with excess burden experienced in neighbourhoods with a higher proportion of immigrants, racialized populations, large households and low socio-economic status.


Line 132: Please confirm whether the authors checked and confirmed that overdispersion was not an issue such that the Poisson distribution is appropriate.
Response: We added the following explanation to line 143: "Models were assessed for zero-inflation by comparing the observed number of zeroes with model predicted number of zeroes for all models. No models were found to be underfitting zeroes. Any overdispersion present in outcomes is accounted for by the use of random effects in all models. [21] 9. Line 137: Indicate how 95% confidence intervals were calculated. Did you use robust standard errors?

Response:
We have recalculated the 95% confidence intervals using robust standard errors, now noted in line 153-4. The results shown in table 2 have been adjusted to reflect the new confidence intervals. This resulted in only slight changes to the confidence intervals, none of which changed the study results.
Response: We added the following description to line 150: "To account for uneven distribution of socio-economic characteristics across neighbourhoods, all model estimates were standardized to show relative risks and 95% confidence intervals of COVID-19 incidence and mortality rates between the 10 th (p10) and 90 th (p90) percentile of each neighbourhood socio-demographic characteristic."

Results:
11. Where possible, please clarify whether you are referring to crude or adjusted rates.
Response: Results were thoroughly reviewed and crude and adjusted risk are now indicated throughout the manuscript. Figure 2. Include in the Methods section how the trend line was estimated.

Line 174-175: Specifically state what the solid black line and dotted lines represent in
Response: A definition of the solid black line is now included in Figure 2 titles. The Methods section now includes the following information regarding how the trend line was estimated on line 154-6: "The distribution of socio-demographic characteristics were plotted against COVID-19 incidence for each neighbourhood, along with solid lines representing the model-predicted estimates (derived using 'prediction' package in R) and dashed lines marking p10 and p90 for each predictor's distribution." 13. Lines 195-196: Indicate the other characteristics that were also not associated with incidence and/or mortality, particularly after adjustment.
Response: Information has been added in lines 206-231.
14. Lines 198-199: You are also controlling for age and sex as confounders, which are also likely playing an important role here.

Response:
We have adjusted language to reflect all confounders include in statistical adjustments.
Lines 229-31 now reads "In adjusted models the protective association of neighbourhoods with the highest compared to lowest proportion of less than high school education was inversed, indicating an association with increased COVID-19 incidence." 15. Lines 211+: Include a summary of results for housing and socio-economic status as well.
Response: Information has been added in lines 219-231

Figures 2A-C -Include the 95% confidence interval
Response: We are happy to include if the editors would like this information. However, our intention in figures 2A-C is to show distribution of COVID-19 incidence across Ontario neighbourhoods sorted by prevalence of 18 socio-economic characteristics. The regression lines assist in interpreting the directionality of association. It is our view that 95% confidence intervals are better suited in Table 2, and that adding more information would decrease the legibility of these figures, which already contain considerable amount of information.

Figures 2A-C -Crop x-axes where there are no data points (Middle Eastern, less than high school are notable)
Response: The figures have been adjusted to crop the x-axis where suggested.

Figures 2A-C -Label all vertical lines (p10, p90)
Response: The figures have been adjusted to label the lines as p10/p90 Discussion:

Line 218: Not directly. Your study examines neighbourhood-level characteristics as determinants of inequities but does not examine specific structural barriers. Suggest rewording for accuracy.
Response: Thank you for this comment. We agree and the sentence has been reworded on lines 241-3 to say: "These findings highlight how neighbourhood-level conditions, which reflect social environments that are influenced by institutional and structural systems (e.g., policy), [22] act as key determinants of COVID-19 inequities." 19. Lines 238-241: If I understand how you constructed your models correctly, your study does not support this conclusion. Did you construct models that included immigration, race, and housing variables together? As I interpreted Table 2, you had separate models for 1) immigration and race, 2) housing, and 3) socio-economic status variables where these groupings were adjusted for age, sex, and urban/rural status. Please clarify.
Response: Thank you for the correction. We have reworded the explanation on lines 263-7 as follows: "In our study, neighbourhood-level housing characteristics were associated with increasing risk of COVID-19 incidence and mortality. Future research is required to examine the extent to which housing characteristics explain the disproportionate impact of COVID-19 on immigrant, racialized, and low-income communities." 20. Line 270: Please include any limitations associated with using a cross-sectional design.

Response:
We added line 319-20 to the limitations section, which states: "Finally, results from our observational study do not allow for causal relationships to be assessed."

Line 271:
Were there changes in testing criteria during this time period? If so, please clarify what these changes were.

Response:
We have added lines 297-9 which describe changing testing criteria: "Testing criteria for SARS-CoV-2 during the study period shifted from initially being restricted to identifying cases in returning symptomatic travelers or individuals with direct exposure to a recent traveler, to being broadly expanded to include asymptomatic individuals in May 2020. [46]"

1.
Line 275: I think this misrepresents the findings of Sundaram's study as significant associations for testing and testing positive were indeed found in fully adjusted models, particularly for variables of importance to this study. I would suggest reconsidering the role of selection bias in your findings.

Response:
We agree our representation of the study findings were too general. We have added the following description to line 301-308: "An Ontario study undertaken concurrently with the current study's period of observation found decreased odds of having been tested for COVID-19 (i.e. communities with higher percentages of lower income and visible minorities) and increased odds of having received a positive COVID-19 diagnosis (i.e., increase quintile of people per dwelling and with limited education attainment) in models adjusted for age, sex, underlying health conditions, previous health care, public health region, environment and area-based social determinants of health.
[47] The resulting under detection suggest the associations between neighbourhood socio-demographic factors and COVID-19 incidence and mortality in our study are likely conservative."

Line 280: I am not sure what you mean by the term 'dilute'. Is there an alternate term that can be used?
Response: We removed the term dilute and instead highlight the differences between area and individual measures. This section now reads: "Additionally, our area-level findings should not be interpreted at the individual-level, as individual cases may not reflect the characteristics of the neighbourhoods they live in. Previous Canadian studies comparing individual and area-level measures have shown that even with relatively poor agreement between measures, area-level measures may be describing important community-level effects that contribute to health inequities. [48]"

Conclusion:
23. Line 296: Include immigration as a key neighbourhood characteristic.
Response: Immigrant was added to the description. Line 327-330 now reads; "Neighbourhood socio-demographic factors, including immigration, race, housing and socioeconomic status are associated with COVID-19 incidence and mortality in Ontario. These results suggest that culturally safe approaches to engaging with immigrant, racialized and low socioeconomic status communities are important public health strategies for reducing COVID-19 inequities." 24. Line 296: This is the first time you mention poverty and this may not be the most accurate term to use hereperhaps low SES or low income communities would be more appropriate to the study context.

Response:
We agree and have adjusted that term. Please see the changes to the description in comment 23.

Reviewer 2:
This paper was an enjoyable read and a good example of how combing datasets can provide valuable insights for policy makers. The methods used to find associations between neighborhood-level sociodemographic measures and COVID-19 incidence and mortality are well articulated and based on sound statistical methods. The comments below are minor and aim to improve the clarity of the paper for the reader. I recommend publications with minor revision.

Lines 33-37: In abstract, IRRs are difficult to interpret as referent and comparison groups are not clear (what do you mean by high proportion). Including details about the deciles would be helpful (comparing 10p -90p).
Response: We added the following description to lines 28-32; "Comparing neighbourhoods in the 90 th to the 10 th percentiles of socio-demographic characteristics, we estimated the associations between 18 neighbourhood-level measures of immigration, race, housing and socio-economic characteristics and COVID-19 incidence and mortality using Poisson generalized linear mixed models."

Line 46: Make it more clear that the South Asian finding was part of the same UK study and make it clear who they have higher odds of mortality compared to
Response: We have clarified with the following sentence. Lines 50-53It now reads: "For example, early in the pandemic a United Kingdom (UK) found it was observed that Black adults have four times higher odds of COVID-19 mortality than White adults in the United Kingdom (UK), with; South Asian and mixed ethnicity individuals also have significantly higher odds of mortality.
[1]" 27. Line 64: Perhaps 'context of Ontario' is more appropriate than 'Canadian context', as you highlight the need for jurisdiction specific findings.

Response:
The suggested change was made on line 70, changing from Canadian to Ontario context.

Line 87: Can you provide a rationale for the dates chosen to define your study period?
Response: In designing our study, we started our observation period to coincide with the beginning of the COVID-19 pandemic and follow up to the most recent COVID-19 incidence and mortality available at the time of submission for publication. We have added this detail to lines 90-94: "We conducted a population-based surveillance cohort study using data extracted from provincial and local reportable infectious disease surveillance systems, collectively known as the Public Health Case and Contact Management System (CCM) which include all known COVID-19 infections and deaths from Ontario, Canada reported between January 23, 2020 and July 28, 2020, the most recent data available at the time of the study. Specifically, recent immigration is defined as having immigrated to Canada within five years of the census date (i.e. 2011-2016), unsuitably crowded housing is based on the number of bedrooms for the size and composition of the household (based on the age, sex, and relationships among household members), low income is considered earning less than the after-tax lowincome cut-off (LICO), and unaffordable housing is defined as spending greater than 30% of income on housing.

Line 113: Listing the four categories would be helpful.
Response: Added line 121.
" Four categories of urban/rural geographic stratification (large urban centre, medium/small urban centre, rural, and remote) were determined by grouping neighbourhoods based on community size, population density, and level of integration with a census metropolitan area or census agglomeration. [20]"